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Linking fish population dynamics to habitat conditions:
insights from the application of a process-oriented approach
to several Great Lakes species
Daniel Hayes ÆMichael Jones ÆNigel Lester ÆCindy Chu ÆSusan Doka Æ
John Netto ÆJason Stockwell ÆBradley Thompson ÆCharles K. Minns Æ
Brian Shuter ÆNicholas Collins
Received: 7 April 2008 / Accepted: 24 December 2008 / Published online: 29 January 2009
ÓSpringer Science+Business Media B.V. 2009
Abstract One of the major challenges facing
fishery scientists and managers today is determining
how fish populations are influenced by habitat
conditions. Many approaches have been explored
to address this challenge, all of which involve
modeling at one level or another. In this paper, we
explore a process-oriented model approach whereby
the critical population processes of birth and death
rates are explicitly linked to habitat conditions.
Application of this approach to five species of Great
Lakes fishes including: walleye (Sander vitreus),
lake trout (Salvelinus namaycush), smallmouth bass
(Micropterus dolomieu), yellow perch (Perca flaves-
cens), and rainbow trout (Onchorynchus mykiss),
yielded a number of insights into the modeling
process. One of the foremost insights is that
processes determining movement and transport of
fish are critical components of such models since
these processes largely determine the habitats fish
occupy. Because of the importance of fish location,
D. Hayes (&)M. Jones J. Netto J. Stockwell
B. Thompson
Department of Fisheries and Wildlife, Michigan State
University, 13 Natural Resources Building, East Lansing,
MI 48824-1222, USA
e-mail: hayesdan@msu.edu
N. Lester B. Shuter
Harkness Laboratory of Fisheries Research, Aquatic
Research and Development Section, Ontario Ministry of
Natural Resources, 2140 East Bank Drive, Peterborough,
ON K9J 7B8, Canada
C. Chu N. Collins
Biology Department, University of Toronto at
Mississauga, 3359 Mississauga Road, Mississauga,
ON L5L 1C6, Canada
Present Address:
C. Chu
Environmental and Life Sciences Graduate Program,
Trent University, 1600 West Bank Drive, Peterborough,
ON K9J 7B8, Canada
S. Doka C. K. Minns
Great Lakes Laboratory for Fisheries and Aquatic
Sciences, Fisheries and Oceans Canada, P.O. Box 5050,
867 Lakeshore Road, Burlington, ON L7R 4A6, Canada
Present Address:
J. Netto
US Fish and Wildlife Service, 4001 N Wilson Way,
Stockton, CA 95205, USA
Present Address:
J. Stockwell
Gulf of Maine Research Institute, 350 Commercial St.,
Portland, ME 04101, USA
Present Address:
B. Thompson
US Fish and Wildlife Service, Western Washington Fish
and Wildlife Office, 510 Desmond Drive SE, Suite 102,
Lacey, WA 98503, USA
123
Rev Fish Biol Fisheries (2009) 19:295–312
DOI 10.1007/s11160-009-9103-8
an individual-based model appears to be a nearly
inescapable modeling requirement. There is, how-
ever, a paucity of field-based data directly relating
birth, death, and movement rates to habitat condi-
tions experienced by individual fish. There is also a
paucity of habitat information at a fine temporal and
spatial scale for many important habitat variables.
Finally, the general occurrence of strong ontogenetic
changes in the response of different life stages to
habitat conditions emphasizes the need for a mod-
eling approach that considers all life stages in an
integrated fashion.
Keywords Fish habitat Population processes
Individual-based modeling Great Lakes fishes
Introduction
The joint strategic plan for management of Great
Lakes fisheries, first adopted in 1980 and subse-
quently revised in 1997 (JSP; GLFC 1997)isa
central guide for fisheries management of the Lau-
rentian Great Lakes. The JSP led to the creation of
Lake Committees comprised of representatives from
fishery management agencies with jurisdiction over
each of the Great Lakes (Dochoda and Jones 2002).
First among the charges to these Lake Committees in
the JSP is that each committee should ‘‘define
objectives for the structure of each of the Great
Lakes fish communities’’ (GLFC 1994). These fish
community objectives are intended to guide fisheries
management decision-making by providing targets
against which fisheries performance can be measured.
The JSP also led to the creation of the Habitat
Advisory Board, whose central purpose was to
facilitate the development of environmental objec-
tives to support the fish community objectives. Since
the signing of the JSP in 1980, efforts to develop
environmental objectives have met with limited
success.
Many of the regulatory agencies responsible for
the aquatic environments of the North American
Great Lakes have recognized that the preservation of
fish communities requires the preservation of fish
habitat. However, development of effective habitat
regulations has foundered on the failure of fisheries
science to develop metrics of habitat quantity and
quality that can be reliably used to assess the
sustainability of fish populations. A primary reason
for this failure is the absence of a strong scientific
basis for linking habitat conditions to the fish
populations and communities that depend on these
habitats. Habitat management has generally pro-
ceeded without formal models that predict the
response of fish populations or communities to
changes in their habitat. Yet, such models are
arguably necessary to help identify limitations
imposed on fish populations by current habitat
conditions as well as to guide the selection among
potential habitat management sites or approaches to
alleviate these limitations. Although numerous stud-
ies have been conducted on Great Lakes fish habitat
(e.g., HabCARES conference proceedings, Kelso
1996), proven methodologies linking habitat supply
to fish population dynamics have not yet been
developed.
Over the course of several years, we had devel-
oped models intended to explicitly link habitat supply
to fish population dynamics for a number of Great
Lakes fishes to address the needs for developing
habitat objectives. In the course of developing these
models, we experienced numerous challenges, and
felt that the guidance in the literature was lacking or
scattered. The goal of this paper is twofold. First, we
present general insights into the modeling process
that we have gained through our collective experi-
ence. Our intent is not to provide an in-depth analysis
of all the challenges we faced within these models,
but rather to paint a picture of the fundamental
challenges we faced across species within the context
of our chosen modeling paradigm. Secondly, we
provide a synopsis of commonalities we observed in
factors limiting these fishes. We also provide a brief
overview of some of the current approaches for
linking fish populations to habitat conditions to set
the context for our review.
Current approaches
Several approaches for linking fish population or
community dynamics to habitat conditions have been
developed for freshwater fisheries. The simplest
models that do this are the well-known empirical
habitat-yield models, such as the morphedaphic index
296 Rev Fish Biol Fisheries (2009) 19:295–312
123
(Ryder et al. 1974), regressions of yield on phospho-
rus concentrations (Hanson and Leggett 1982)or
chlorophyll concentration (Oglesby 1977; Jones and
Hoyer 1982). These models describe habitat in a
highly aggregated manner, and thus are most useful
when changes in habitat are of a pervasive nature,
such as might occur when phosphorus loadings alter
the nutrient loading of a large part of a lake basin.
These models are also useful when spatially detailed
data are absent. They are of limited value, however,
for examining how changes in localized habitat
conditions affect fish production or yield. They are
also not well suited to the incorporation of additional
habitat components that are thought to be important
in particular situations.
Another approach for linking fish populations to
habitat is embodied in habitat suitability index (HSI)
models. These models usually treat habitat conditions
at a relatively fine spatial scale. Habitat data are then
combined with observations on the distribution of
individual fish (often of a particular life stage) to
develop utilization curves (e.g., Guay et al. 2000;
Vilizzi et al. 2004). These curves, in conjunction
with expert judgement of species’ habitat require-
ments, are used to convert habitat conditions into an
aggregated measure of habitat quality, often referred
to as weighted usable area (e.g., Williams et al.
1999). The HSI models are sometimes calibrated
against abundance, but more commonly weighted
usable area is assumed to be linearly related to
abundance (e.g., Raleigh 1982; Stalnaker et al. 1995).
These models permit explicit consideration of a
variety of habitat components, but they do little to
elucidate either the mechanism by which individual
habitat components affect fish populations, or the
relative importance of each component (i.e., which
component is limiting). Moreover, much of the
controversy surrounding these models (e.g., Acreman
and Dunbar 2004) concerns the rules used to
combine the individual suitability curves for different
habitat components or for different life stages into an
overall index (e.g., Roussel et al. 1999; Roloff and
Kernohan 1999). For example, habitats that provide
optimal conditions for one life stage may be sub-
optimal for other life stages. Without the link
provided by considering the species’ population
dynamics, it is difficult to justify choices made when
combining suitability measures for different life
stages. Finally, because these models are calibrated
against (or are designed to predict) aggregate indi-
cators of population status such as abundance or
biomass, rather than specific demographic parameters
such as mortality rates, they are not well suited to
explore the interaction between habitat supply and
other factors that affect population dynamics such as
biotic interactions or fishery exploitation.
Similar to HSI models, a number of empirical
models have been developed that correlate multivar-
iate measures of habitat conditions to fish distribution,
habitat utilization, or to indicators of population status
such as stock abundance or biomass (e.g., Bowlby and
Roff 1986; Wagner and Austin 1999; Stoneman and
Jones 2000). These methods provide useful insight
into the ecology of fish and their relationship to
habitat quality, but they suffer from many of the same
limitations as HSI models, especially when used as
predictive tools or incorporated into population or
community models.
Another class of models attempts to explicitly link
habitat conditions with the vital rates of populations
(see review by Rose 2000) to provide predictions of
fish response across a variety of scales. We have
argued this approach may prove more successful in
providing the foundations for a model-based habitat
management system (Hayes et al. 1996; Minns et al.
1996). Although some exceptions exist (e.g., Shuter
1990; Marschall and Crowder 1996; Minns et al.
1996; Rose et al. 1999; McDermot and Rose 2000),
few of these models cover all of the principal vital
rates (i.e., birth rate, survival rate, individual growth
rate), or follow through the entire life cycle of the
model species. Frequently, such models have focused
on a particular life stage or one vital rate (e.g., Brandt
and Kirsch 1993; Mason et al. 1995).
In the research described here, we used the
approach underlying these models as a basis for the
development of models for several Great Lakes fish
species that cover all vital rates and the full life
history for the majority of these species. To test the
robustness and feasibility of implementing this
approach, we selected Great Lakes fish species with
contrasting life histories. These species included:
walleye (Sander vitreus), lake trout (Salvelinus
namaycush), smallmouth bass (Micropterus dolo-
mieu), yellow perch (Perca flavescens), and rainbow
trout (Onchorynchus mykiss). Except rainbow trout,
we modeled the full life cycle of the population of
interest. In the next section we briefly describe these
Rev Fish Biol Fisheries (2009) 19:295–312 297
123
models in order to provide background supporting
the set of insights and advice we present in our
review. In these descriptions, we emphasize the
major features of each of the species-specific models
to provide an understanding of the common data
requirements and modeling approaches used to link
individual life stages with their surrounding habitat
(Table 1).
Overview of individual species models
Details on the models developed for each species
are available in Chu et al. (2006), Thompson (2004),
Netto (2006), Jones et al. (2003), and Doka (2004).
In the descriptions below, we emphasize the major
features of each species-specific model. The
approach we took in modeling the species selected
is not fundamentally different than other approaches
that have been previously used (e.g., Marschall and
Crowder 1996; Minns et al. 1996; Rose et al. 1999).
Rather, using these studies as a base, our choice of
specific modeling methods was guided by the
situation presented for each species. All models
shared a number of common elements, however. A
common theme was a focus on population processes
of births, deaths and individual growth, leading to a
cohort-based, age-structured description of dynam-
ics. In all the models, at least one life stage was
represented as an individual-based model, or a
pseudo-individual based model where a cohort was
broken into spatially or temporally explicit sub-
groups. This led us to use descriptions of habitat
conditions and biological inputs at relatively fine
scales. As our modeling efforts progressed, we
found that in addition to the key population-level
vital rates listed above, the incorporation of fish
transport and movement behavior became a vital
consideration. Therefore, the basic concept that links
all our models is the idea that at a particular point in
time, the state of an individual fish must be
described in terms of its size and location, as well
as other features such as sex and age. Between
points in time, the fish can either survive or die, and
it can remain at its current location or move. The
probability of survival or movement, as well as the
growth rate of individual fish is dependent on their
state variable descriptors (e.g., size, age, location),
the environmental conditions they are exposed to at
their current location, and random chance (Fig. 1).
From this basic building block, different life stages
are linked in time and space, with vital rates being
dependent on environmental conditions at each stage
(Fig. 2).
Table 1 Habitat components influencing growth, survival or movement of specific life stages for each species modeled
Habitat characteristic Life stage
Egg Larvae Juvenile Adult
Temperature W, B, L, P, R W, B, P W, B, L, P, R W, B, P
Substrate W, B, L, P, R P P P
Depth W, B, L, P, R P P P
Water velocity or current W, L, R W, P W, L
Light W
Wave action B, P B
Total dissolved solids B
pH BB
Dissolved oxygen B B
Suspended sediments L
Macrophytes P P P P
Stream mesohabitat type R
Density of conspecifics P, L W, B, P, R B, P
Food abundance W, P, L W, B, L, P, R W, B, P
Species abbreviations are as follow: W, walleye; B, smallmouth bass; L, lake trout; P, yellow perch; R, rainbow trout
Note that the main focus of rainbow trout was the egg and juvenile stages
298 Rev Fish Biol Fisheries (2009) 19:295–312
123
In developing these models, we relied primarily on
published studies to define suitable habitat conditions
and to relate key processes to those habitat condi-
tions; field sampling or experiments were conducted
only for rainbow trout and smallmouth bass. When
evaluating relationships presented in the literature,
we made an attempt to explicitly characterize effects
as density-dependent or density-independent, recog-
nizing that factors operating in these different
fashions have different implications for population
dynamics (Hayes et al. 1996).
Western Basin of Lake Erie walleye (Jones et al.
2003)
An outline of the critical elements of the walleye
model is provided in Fig. 3. One of the key details of
the walleye population in the Western Basin of Lake
Erie is that adults reside in the open lake during most
of the year, but a portion of the population spawns in
offshore reefs while another part of the population
spawns in tributary rivers. At present, the proportion
of the population spawning on reefs and in tributaries
is unknown, but reef habitats are far more extensive.
As such, we apportioned the population as 90% reef-
spawning and 10% as tributary-spawners. The wall-
eye model starts with adult walleye that produce
eggs based on their sex ratio, size distribution and
maturation schedule. Spawning times depend on
water temperature.
For lake spawners, eggs are deposited in offshore
reefs following observations of Roseman (2000). The
development of eggs is a function of water temper-
ature, and their numbers are reduced by a constant
daily mortality rate, plus a depth-dependent episodic
mortality during high wind events causing distur-
bance of the spawning habitat (Roseman 2000;
Roseman et al. 2001). Following hatching, larval
walleye are transported by wind-driven surface water
currents (Roseman 2000). Larvae transported off-
shore are assumed to die, and larvae transported
onshore remain in the nearshore zone where their
growth and survival is modeled as a function of water
temperature and prey abundance.
Selection of spawning habitat in the Sandusky and
Maumee Rivers (the principal rivers used for spawn-
ing in the Western Basin) by adult walleye is
modeled as a process where the best habitat (defined
on the basis of substrate size) is selected first, and
after saturation, less preferred habitats are selected
secondarily. Survival rate of eggs varies with sub-
strate size, and the development rate of eggs varies
with water temperature. After hatching, survival rate
and downstream transport of larvae depends on river
discharge and temperature, following Mion et al.
(1998). The growth of larvae while in the rivers is
negligible because of the lack of zooplankton (Mion
et al. 1998).
After reaching the lake itself, river-spawned larvae
are assumed to be exposed to the same environmental
conditions as reef-spawned larvae, although their size
distribution frequently differs due to differences in
spawning times. Larval and juvenile walleye growth
and survival during the first year of life are modeled
as a function of density and food abundance. We
found no quantitative information in the literature
to suggest how other habitat conditions (e.g.,
Fish(ID, time,
location, size)
Dies
Survives
Function of environmental
conditions, size and growth,
location and movement, fish
densit
y
and random chance
Doesn’t
move,
grows
Moves,
grows Fish(ID, time+1,
new_location, new_size)
Fish(ID, time+1,
location, new_size)
Fig. 1 Conceptual model of processes underlying the popula-
tion models we used
Eggs
Larvae
Juvenile
Adults
Temp Substrate Current Depth Light Oxygen Food
Space
Time
Fig. 2 Connection between different life stages across time
and space, and the dependence of vital rates on habitat
conditions
Rev Fish Biol Fisheries (2009) 19:295–312 299
123
macrophytes, substrate size composition) affect the
growth, survival or distribution of walleye during the
first year of life.
Following the first winter of life, the principal
habitat conditions determining the growth rate of
juvenile and adult walleye are light, temperature, and
prey abundance, with light and temperature defining
the volume of the lake where habitat conditions are
acceptable. Mortality rate was assumed to be a
constant rate of natural mortality plus fishing mor-
tality on fish of legal size/age.
Smallmouth bass (Chu et al. 2006)
Two habitat sub-models were defined for smallmouth
bass based on the habitat requirements of the
different life stages (Fig. 4). The first focuses on
the nesting habitat and includes reproductive adults,
eggs, hatchlings, swim-up fry and YOY (post-
dispersal and for the remainder of the growing
season). The second includes the juvenile and adult
life stages. The adult life stage is defined as fish age
C3 years because that is the age when these fish
approach maturity.
Available habitat is represented as a grid, with
temperature, substrate, depth, fetch and wave action
described for each grid element. Using the habitat
data, the model maps the suitability of different sites
in a lake for nesting and for the juveniles/adults.
Spawning timing is dependent on water temperature,
and egg production by mature smallmouth bass is
modeled as a function of their size distribution and
sex ratio. Ideal free distribution theory (e.g., Fretwell
and Lucas 1970) is used to determine the spatial
distribution of nests, juveniles (age 1?to maturity)
and adults throughout the lake. Growth of the young-
of-the-year is dependent on total dissolved solids,
fetch and temperature, and is also density-dependent.
Survival is size-dependent in the young-of-the-year,
but is set at a constant annual rate for older fish.
Growth of age 1?smallmouth bass is dependent on
an index of productivity, temperature, and density. A
Mature adults
-fecundity dependent on size distribution
Reef spawning component
-spawning sites selected based on substrate, depth
-spawning timing based on water temperature
-egg survival based on wind-induced currents,
-egg development based on temperature
River spawning component
-spawning sites selected based on substrate, depth
-spawning timing based on water temperature
-survival dependent on substrate type,
-development based on temperature
Larval Stage
-transport dependent on wind-induced currents
-survival dependent on temperature, food abundance
-growth dependent on temperature, food abundance
Larval Stage
-transport dependent on river discharge (velocity)
-survival dependent on temperature, river discharge
-growth is minimal while in river
Juvenile Stage (to age 1)
-early transport dependent on wind-induced currents, later able to maintain location
-survival dependent on temperature, food abundance
-growth dependent on density, food abundance
Juvenile and Adult Stage (age 1+)
-able to actively seek preferred habitat conditions and maintain location
-survival determined by natural mortality plus fishing mortality
-growth dependent on volume of suitable habitat, defined by temperature and light preferences
Fig. 3 Flowchart of
walleye population model,
emphasizing key
components and driving
variables for each life stage
300 Rev Fish Biol Fisheries (2009) 19:295–312
123
home range mechanism is used to model density-
dependent effects on growth for all life stages.
Lake trout (Netto 2006)
Egg deposition by adult, wild-spawned lake trout is
dependent on the sex, size and age distribution of the
adult population (Fig. 5). The initiation of spawning
behavior is triggered by suitable water temperature,
and spawning sites are selected based on substrate
size composition and water depth. Egg development
is temperature-dependent, and survival of eggs and
yolk-sac fry depends on the magnitude of wind-
induced water current velocity and the amount of
sediment resuspension. After emergence, early juve-
niles are transported by wind-induced currents, but
are able to maintain their location within several
weeks of emergence. The growth and survival of
juvenile lake trout is modeled as a function of water
temperature and food abundance. Conceptually,
growth, survival and distribution of post-juvenile
lake trout depends on prey abundance, temperature,
and depth, but finding studies linking vital rates to
environmental conditions proved to be problematic.
Yellow perch (Doka 2004)
As with the other models, egg deposition by yellow
perch depends on the size structure and sex ratio of
the population (Fig. 6). Spawning sites are selected
based on substrate characteristics, water depth, and
the presence of macrophytes (Weber and Les 1982;
Fisher et al. 1996; Robillard and Marsden 2001), and
the timing of spawning is based on water tempera-
ture. The development of eggs is also temperature-
dependent, with survival dependent on temperature
and wind-induced currents and wave action (as
determined by wind speed, wind direction and fetch).
Following hatching, planktonic larval yellow
perch are distributed by water currents, and their
Mature adults
-fecundity dependent on size/age distribution
Spawning and Nesting
-spawning sites selected based on substrate, depth and fetch, wave action
-spawning timing based on water temperature
-egg development based on temperature
-young-of-the-year growth dependent on total dissolved solids, fetch, temperature
-young-of-the-year survival dependent on growth
Juvenile and Adult Stage (age 1+)
-able to actively seek and maintain location in preferred habitat conditions
-preference based on temperature, pH, oxygen, fetch
-survival determined by natural mortality and fishing mortality,
-growth dependent on productivity index, temperature, and fish density
Fig. 4 Flowchart of
smallmouth bass population
model, emphasizing key
components and driving
variables for each life stage
Rev Fish Biol Fisheries (2009) 19:295–312 301
123
growth and survival is determined by water temper-
ature and habitat-based, indirect measures of
zooplankton prey abundance (Ross et al. 1977;
Thorpe 1977). After reaching approximately
30 mm, juvenile yellow perch become demersal and
are no longer vulnerable to relocation by water
currents. Survival and growth are dependent on
temperature, substrate (as an index of food availabil-
ity), and macrophyte density (as a surrogate for
relative predation risk). We also explored options for
density-dependent growth and survival. As juvenile
yellow perch age and grow, they become increasingly
able to select preferred habitats, and the model allows
for such movements. As adults, yellow perch are
modeled as being able to select preferred habitats,
and their growth is dependent on temperature and
relative food availability in a density-dependent
fashion. Mortality of juveniles and adults varies with
growth, and also includes fishing mortality. The
majority of the simulation work was focused on the
stages from adult spawning to completion of the first
season by new fry.
Rainbow trout (Thompson 2004)
Unlike our other target species, we did not construct
a full life-cycle model for rainbow trout. Our
approach for this species was somewhat different
because we were able to conduct field sampling and
experiments in the Pine River, Alcona County,
Michigan (Thompson 2004). For this species, our
focus was on the dynamics of age-1 rainbow trout
(Fig. 7), emphasizing their movement dynamics and
growth rate as a function of habitat conditions. The
growth of age-1 rainbow trout is dependent on river
branch-specific, empirically derived estimates of
consumption rates and site-specific temperature
regimes. These inputs were incorporated into the
‘‘Wisconsin’’ bioenergetics model (Kitchell et al.
1977) to model growth. Movement was represented
as a stochastic process where the probability of
movement among mesohabitat units was modeled as
a function of mesohabitat designations (i.e., pool,
riffle, run) and density of juvenile rainbow trout. We
conducted a census of the mesohabitat units of over
Mature wild-spawned adults
-fecundity dependent on size/age distribution
Spawning and yolk-sac stage
-spawning sites selected based on substrate, depth
-spawning timing based on water temperature
-survival based on wind-induced currents and sediment resuspension
-development based on temperature
Juvenile Stage (to age 1)
-early transport dependent on wind-induced currents, later able to maintain location
in preferred habitats
-survival dependent on temperature, food abundance
-growth dependent on temperature, food abundance
Adult Stage (age 5+)
-survival dependent on temperature, food abundance, depth
-growth dependent on temperature, food abundance
Yearling to Adult (age 1-4)
-survival dependent on temperature, food abundance, depth
-growth dependent on temperature, food abundance
Fig. 5 Flowchart of lake
trout population model,
emphasizing key
components and driving
variables for each life stage
302 Rev Fish Biol Fisheries (2009) 19:295–312
123
70 km of the Pine River, classifying each unit as a
pool, riffle, or run. Additional habitat variables such
as river width, depth, and substrate composition
were also collected, but were not incorporated into
the final model.
Insights and perspectives
Individual-based models are needed
One characteristic we judged to be necessary for all
of the models we developed was that key population
processes (i.e., vital rates such as birth, death,
individual growth rate, as well as movement) needed
to be explicitly linked to environmental factors
experienced by individual fish, or small groups of
similar individuals. While other, more aggregated
representations of these dynamical processes are
possible (e.g., Leslie matrix), we found it necessary
to use an individual-based approach because it
allowed us to readily represent the spatial distribution
of individuals among habitat patches, leading to
important differences in their vital rates. Explicit
incorporation of the spatial distribution was necessary
to represent the range of environmental conditions
experienced by individuals in the population, and to
model changes in the spatial distribution of fish in
response to environmental conditions. Individual-
based models also allow for the incorporation of
individual variation in important characteristics such
as body size and hatch date. These variations,
Mature adults
-fecundity dependent on size distribution
Spawning
-spawning sites selected based on substrate, depth, macrophytes
-spawning timing based on water temperature
-egg survival based on wind-induced currents as affected by fetch
-egg development based on temperature
Planktonic Larval Stage
-transport dependent on wind-induced currents as affected by fetch
-survival dependent on temperature, food abundance
-growth dependent on temperature, food abundance
Juvenile Stage (to age 1)
-active movement among grid locations based on habitat conditions
-survival dependent on temperature, depth, substrate, and macrophyte density
-options for density dependence
-growth dependent on temperature, depth, substrate, and macrophyte density
Adult Stage (age 1+)
-active movement among grid locations based on habitat conditions
-survival dependent on temperature, depth, substrate, macrophyte density, and fishing
-options for density dependence
-growth dependent on temperature, depth, substrate, and macrophyte density
Demersal Larval Stage
-transport is minimal; some active movement
-survival dependent on temperature, depth, substrate, and macrophyte density
-options for density dependence
-growth dependent on temperature, depth, substrate, and macrophyte density
Fig. 6 Flowchart of yellow
perch population model,
emphasizing key
components and driving
variables for each life stage
Rev Fish Biol Fisheries (2009) 19:295–312 303
123
coupled with differences in habitat conditions expe-
rienced by individuals can lead to important
implications for fish population dynamics. For exam-
ple, in our walleye model, variations in hatch dates
allows for the population to ‘‘sample’’ variations in
river flows, thereby increasing the odds that some
individuals will experience favorable conditions early
in life (Jones et al. 2003). Likewise, variations in
hatch dates were important components of our
models for yellow perch and smallmouth bass.
Similar results highlighting the importance of indi-
vidual variation have been obtained in a variety of
systems for numerous species (e.g., Miller 2007;
Hook et al. 2007).
We had initially proposed to avoid the extreme
reductionism and data demands of individual-based
models, but were unable to find simpler, yet realistic
alternatives for modeling these processes. As such,
we agree with the recommendations of Lomnicki
(1999), Juanes et al. (2000), and Rose (2000), among
others, that individual-based models are generally the
best approach for addressing questions regarding the
linkage between (fish) populations and their habitat.
We note, however, that there are serious implications
of choosing an individual-based modeling approach.
One of the major implications, which we discuss
further below, is that data on the variance and shape
of the statistical distribution, rather than just the
mean, of many population processes is required to
implement individual-based models to their full
advantage. These data requirements are very difficult
to meet, leading modelers to make assumptions on
the parameters, or borrow data from other species,
both of which may lead to substantial model
uncertainty.
Another positive feature of the individual-based
approach is that these models are amenable to the
direct incorporation of factors such as fishery harvest
or competition. Although we consciously excluded
such factors in order to limit the complexity of our
models, we feel that the modeling approach is robust
enough to readily incorporate such factors. A final
advantage of individual-based models is that they
tend to favor conceptual simplicity over computa-
tional and analytical simplicity or elegance. This
conceptual simplicity greatly facilitates the commu-
nication and discussion of model structure to
scientists and policy makers (e.g., Beck et al. 2001).
Mature adults
-fecundity dependent on assumed size/age distribution
Spawning and in-gravel yolk-sac fry
-spawning sites selected based on substrate, depth, velocity or mesohabitat type
-spawning timing based on water temperature
-survival assumed constant
-development based on temperature
Juvenile Stage (to outmigrationat age 2+)
-movement and habitat selection based on temperature, mesohabitattype, and fish
density, calibrated with experimental data
-survival dependent on temperature, growth, and movement
-growth dependent on temperature, and empirically-derived branch-specific consumption rate
YOY stage
-passive transport early, later able to maintain position
-survival assumed constant
-growth estimated from field data
Fig. 7 Flowchart of
rainbow trout model,
emphasizing key
components and driving
variables for each life stage
304 Rev Fish Biol Fisheries (2009) 19:295–312
123
Ontogenetic shifts and habitat juxtaposition are
important
Another feature common to all of our models was the
need to accommodate ontogenetic shifts in habitat
requirements, and thus the spatial distribution and
connectivity among habitats used by different life
stages (Schindler and Scheuerell 2002). Because
successive life stages often have different habitat
requirements and limitations, simple HSI models are
likely to be seriously deficient without a means to
connect the dynamics of different life stages.
Although births and deaths are the only processes
that ultimately determine abundance in closed pop-
ulations, we found that the movement or transport of
fish often turned out to be a key process affecting
survival and reproductive success. For all species,
representing this movement was one of the greatest
challenges we faced in our modeling efforts. The
movement of early life stages of many species occurs
through passive transport by water currents. Thus, for
example, we had to represent surface water currents
in the Western Basin of Lake Erie to predict transport
of reef-spawned larval walleye. Our approach was to
use a simple rule, developed by Olson (1950) where
surface current velocity is 10% of wind velocity and
10°to the left, to provide a first approximation.
More complex models of water currents exist for
many areas in both marine (see in particular review by
Miller 2007) and large freshwater systems such as the
Great Lakes (e.g., Beletsky et al. 2007; Zhao et al.
2009). Such models have provided substantial insight
into the transport processes of larval fishes, but in our
situation, none were available for the particular
regions inhabited by our target species at the time we
developed these models. A further concern is that even
if models are available, the prediction of fish transport
may not be improved. This occurs because water
currents in large lakes and the ocean have a complex
three-dimensional pattern (e.g., Saylor and Miller
1987; Royer et al. 1987; Quinlan et al. 1999). Thus, at a
given two-dimensional location (i.e., latitude and
longitude), water current velocity and direction varies
with depth and lake bathymetry (Schwab and Bennett
1987). For example, surface currents may run at 0.1 m/
s in a SW direction, but the current at 5 m in depth may
run 0.05 m/s in a NE direction. The vertical distribu-
tion of larval fishes is generally not well known, and
further, can vary even on a diel basis (e.g., Houde
1969), possibly in response to water currents. Thus, the
problem of predicting the passive transport of larval
and juvenile fishes in the Great Lakes remains a major
challenge, but the importance of such transport on the
demographics of young fishes emphasizes the need to
continue working in this area.
As fish age and are able to actively move against
water currents, the situation becomes no less com-
plex. A number of theories, such as optimal foraging
theory (e.g., Mittelbach 1981) and the ideal free
distribution (e.g., Tyler and Hargrove 1997), have
been developed to predict the habitat choice and
equilibrium distribution of fish in a heterogeneous
environment. Unfortunately, the constantly changing
mosaic of environmental conditions results in a
transient state of habitat dynamics, a situation where
these theories are not readily applicable. One strength
of our modeling approach is that the models provide
predictions of transient dynamics as well as equilib-
rium outcomes. For one species (rainbow trout), a
major component of our field work was to perform
tagging studies combined with experimental translo-
cation to better understand the transient dynamics of
fish movement and habitat selection. In general, we
found that habitat selection at the mesohabitat (i.e.,
pool, riffle, run) level by river-dwelling fish was
somewhat easier to handle than for lake-dwelling fish
because rivers can be treated as linear geographic
features with directional flows, whereas location
within a lake is a three-dimensional geographic
feature. For open lake and marine systems, some
new approaches using mechanistic behavioral models
show promise for simulating the movement of free-
ranging fishes (Humston et al. 2004).
Paucity of published studies relating habitat
conditions to vital rates
A major challenge we faced in all models was finding
published studies relating population vital rates to
habitat conditions. For example, a number of studies
have documented that recruitment of walleye is
correlated to the rate of springtime warming and the
severity of wind events (Busch et al. 1975; Roseman
2000). While these studies suggest a linkage between
aggregated descriptions of habitat conditions and
recruitment success, they provide little information
regarding how survival rate of juvenile walleye (for
example) varies with water temperature, or how egg
Rev Fish Biol Fisheries (2009) 19:295–312 305
123
survival rate on reefs varies with wind and water
velocity. Likewise, there are abundant data describ-
ing the habitat conditions where fish are collected,
putatively indicating habitat selection, but these data
also provide little insight into how population vital
rates vary across habitats. However, some encourag-
ing insights into these associations are provided by a
recent study (Zhao et al. 2009) that used a three-
dimensional hydrodynamic model to correlate cohort
strength with current-driven, reef-to-shore movement
of larval walleye in western Lake Erie.
We experienced similar difficulties with each of
the species investigated, and for virtually all life
stages. Even for adult stages, which are generally
easier to capture and mark or follow using telemetry
there is a dearth of data. Thus, we were often forced
to make assumptions regarding how survival in
particular varied across habitats.
It is difficult to determine the specific reasons why
there are so few studies relating population processes
to habitat conditions. We offer two explanations for
this observation (1) scientists generally don’t con-
ceptualize or frame fisheries problems this way; or (2)
it is difficult and expensive to measure mortality and
growth rates for fish over short enough time scales to
assign these rates to particular habitat conditions. We
hope that papers like this will help shape scientists’
thinking when developing studies of fish-habitat
relations. Thus, where suitable data are collected to
ascertain habitat-specific vital rates, we hope
researchers pursue such opportunities. Studies using
telemetry or acoustic tags appear particularly suited
to estimating survival rates, for example, as a
function of habitat conditions. The second impedi-
ment, however, remains a challenge to fishery
scientists and fish ecologists. Our experience with
field studies provides several insights. First, growth
rate estimates are relatively easier to obtain over short
intervals than other vital rates. Partly this occurs
because the unit of observation is an individual fish.
Individual tagging provides a means of estimating
growth rate of individual fish, thereby providing point
estimates and even the distribution of growth rates
under specified habitat conditions. We were able to
successfully apply this approach to rainbow trout in
the Pine River, Michigan (Thompson 2004). We
would note, however, that even in systems that are
readily sampled, fish movement out of the study zone
can limit recaptures, and that recapture rates of
marked individuals rarely approaches 100%. Thus,
even growth rates for individuals can be challenging
to measure directly in the field. Estimating habitat-
specific mortality rate has proven even more difficult.
Partly this occurs because losses due to mortality are
generally confounded with movement out of the
habitat units. This is particularly a problem in ‘‘poor’’
habitat where fish often move away from such
conditions before mortality takes place. Further,
mortality rates are often estimated for the (sub)
population as a whole, making the unit of observation
the (sub) population; as a consequence, many exper-
iments need to be conducted in order to determine
variability among habitats. Finally, under ‘‘good’’
habitat conditions, mortality rates can be very low,
and difficult to estimate precisely for short time
intervals. As indicated above, telemetry studies hold
promise to estimating mortality rates, but such studies
are often expensive to conduct.
In addition to an understanding of the relation
between vital rates and habitat conditions, knowledge
of fish movement as a function of habitat conditions
is also important. Developing movement rules for fish
is an area where recent research highlights the utility
of using individually marked fish (e.g., Railsback and
Harvey 2002; Belanger and Rodriguez 2002). These
approaches are particularly appealing within the
modeling framework we present here because they
focus on the behavior of individuals. Humston et al.
(2004) present a useful summary of some mechanistic
models relating fish movement to habitat conditions.
In particular, movements based on kinesis or a
gradient response based on a restricted-area search
appear promising ways of connecting fish movement
and location to habitat conditions. For smallmouth
bass, we had sufficient information available to use
the concept of the ideal free distribution to help guide
our models of fish movement and habitat choice.
One linkage between the field experiments and our
overall modeling approach is the use of inverse
modeling techniques (e.g., Parker 1977; Nibbelink
and Carpenter 1998) to infer vital rates that are
consistent with field data. In particular, we were able
to infer ‘‘movement rules’’ for rainbow trout in
streams, differentiating probability of movement
among pools, riffles and runs, as well as a function
of temperature (Thompson 2004). In many cases,
prior studies contain data that were collected at a
more aggregated level than our level of treatment
306 Rev Fish Biol Fisheries (2009) 19:295–312
123
(e.g., growth and survival of individual fish depends
on microhabitat conditions). In inverse modeling,
trial values of system parameters are evaluated and
adjusted to match observed data, thereby allowing
inferences on processes that are not directly observed.
This is a particular advantage if field data are not
collected on a habitat-specific level, because model-
ing inferences can be developed to ‘‘explain’’ the
observations. While this is a powerful and useful
approach, several caveats must be kept in mind. First
and foremost, it is often very difficult to resolve
among competing model structures. Thus, the infer-
ences being made are conditional on having the
‘‘right’’ model structure in place. Further, parameters
are often highly confounded if the level of data
resolution (spatial, temporal, or level of process
description) does not match that represented in the
model.
Paucity of relevant habitat data
In addition to a dearth of studies relating vital rates to
habitat conditions, data for many of the critical
environmental/habitat conditions are lacking at a
‘‘reasonable’’ temporal or spatial scale and sampling
intensity to be useful. Technological advances in
geographic information systems (GIS), monitoring
devices such as flow gauges, and remote sensing are
improving our ability to collect relevant habitat data
but these data are still lacking in many situations.
Temporally intensive data collections tend to be
spatially very limited, and conversely spatially
extensive data tend to be limited to few time periods.
Because of this, we often had to use some means for
describing habitat conditions based on sparse data.
We used two approaches for describing the dynamics
of habitats (1) a process-driven approach where we
modeled the underlying factors driving habitat con-
ditions (e.g., circulation modeling in lakes); and (2) a
data-driven approach where habitat conditions are
estimated by interpolation from surrounding times
and locations.
In the first approach, mechanistic sub-models
representing the underlying dynamics of habitat
conditions were developed. An example of this is
the wind-driven, water current sub-model we used for
Lake Erie walleye. In this sub-model, water current
velocity and direction were predicted from data
available on wind speed and direction. Our
representation was by necessity relatively simple;
more sophisticated circulation models have since
been developed (e.g., Zhao et al. 2009). One strength
of this approach is that data on the habitat conditions
of interest are not required if data on other, driving
variables are available. A similar approach could be
used in streams where landscape-scale features can be
used to drive habitat models or can serve as proxies
for habitat conditions present (e.g., Wiley et al.
1997). Often such approaches provide a means to
estimate some driving factors, such as water temper-
ature, that are driven by broad-scale factors, but are
often not suitable to provide a picture of finer-scale
habitat features such as the location of individual
pools in a stream.
In the second approach, habitat conditions over
the entire region are interpolated from existing data
(Doka 2004; Kratzer et al. 2006). This too creates a
model of habitat dynamics, making assumptions
regarding how these conditions vary across space
and time, but these assumptions are not based on
mechanistic processes. For example, water temper-
ature data for Long Point Bay were available at a
limited number of locations and sampling dates.
With these data and satellite imagery of the entire
bay, an interpolation model predicting the water
temperature at intermediate times and locations was
constructed (Doka 2004). This model assumed
substantial coherence in temperature patterns at a
large spatial scale, but finer scale patterns (e.g.,
upwelling events) were generally preserved by the
combined information from temperature dataloggers
and spatially explicit thermal imagery. Interpolations
further assume some regularity in the vertical
temperature structure during the summer stratifica-
tion period. Even a coarse estimate of temporal and
spatial differences in physical variables (especially
temperature) is more realistic than using a single
profile to represent an entire water body. In general,
we feel that the interpolation approach is particu-
larly useful when data on habitat conditions are
available at a reasonable sampling intensity to
predict interpolated points, and when the habitat
conditions themselves are relatively continuous and
coherent across space and time. An example of
where the interpolation approach is less useful is
predicting water currents, which often show abrupt
changes in time and space scales that are much
shorter (e.g., hours to days) and finer (e.g., within 1–
Rev Fish Biol Fisheries (2009) 19:295–312 307
123
2 m vertically) than those represented in typical
sampling designs.
As another example of how data intensive these
models can be, we performed a ‘‘census’’ of meso-
habitat conditions for over 70 km of the Pine River,
Michigan to support our rainbow trout model
(Thompson 2004). Even though it is a relatively
straightforward task to collect such data, it is very
time and labor intensive. Further, our data collections
did not extend completely into the headwaters of this
river system as this was beyond the scope of our
study. Fortunately, such stream features are often
relatively stable over time, allowing for habitat
surveys to be conducted once over the course of
several years, and then treated as ‘‘fixed’’ habitat
features. We also collected transect-level habitat data
(e.g., stream width, depth, substrate composition),
however the scale of these data was finer than the
mesohabitat scale we eventually chose. Even though
these data would in principle have been useful in
further detailing habitat condition available, imple-
mentation in our model would have required a much
more intensive sampling of the entire river reach than
would be feasible. As this example shows, there are
obvious tradeoffs between the level of detail and
scale of habitat information and the spatial extent that
can be sampled with a given set of resources. It is not
so obvious, however, which scale is ‘‘best’’. For this
species in this setting, we chose a greater spatial
extent of sampling because preliminary sampling
showed age-1 rainbow trout to be highly mobile,
often covering distances greater than 1–2 km within
1 week or less.
The broad time scale across which habitats vary
also presents several challenges. For example, tem-
perature patterns in the environment often change
over short time periods (e.g., days), but other features
such as bathymetry or channel morphology often
change slowly (e.g., years to decades). This can result
in ‘‘stiff’’ systems (borrowing the term used for
differential equations; Press et al. 1992) where the
dynamics of different components need to be treated
differently. The varying time scales for habitat
variation also have implications for sampling inten-
sity necessary to represent habitat conditions
adequately. Some features, such as bathymetry, are
likely to change relatively slowly, allowing for
sampling to be temporally less intensive. Other
features, such as stream water temperature, which
may vary several degrees within a day, may require
almost constant measurement. Most challenging,
perhaps, are habitat conditions that rapidly vary both
spatially and temporally. Water temperature in Great
Lakes embayments is an example of such a situation.
This was a particular problem for our yellow perch
model because of their broad distribution within Long
Point Bay. During periods of stratification develop-
ment or decay, the water temperature at a given point
can change within a few days, and the difference in
temperature between adjacent points (e.g., points
along a vertical thermal profile) may likewise change
rapidly. Cases such as these are difficult to address
with either mechanistic models or interpolation
models because of the abrupt changes that may
occur. Therefore, the choice of time step in habitat-
based models is important, as well as the availability
of data or physical models as input (Minns and
Wichert 2005).
Common outcomes across species
Across our model species, several habitat features
showed a pervasive effect on model outcomes.
Foremost among these was water temperature
(Magnuson et al. 1979). Water temperature has long
been known to strongly affect growth (e.g., Kitchell
et al. 1977; Hewett and Johnson 1992), survival and
development rate of fishes (e.g., Allbaugh and Manz
1964; Hurley 1972), and further can strongly affect
their spatial distribution (e.g., Mason et al. 1995).
Because of its effect on multiple vital rates and fish
behavior, changes in water temperature can have a
disproportionate effect on aggregated outputs such as
production, biomass and abundance. As such, obtain-
ing adequate data on spatial and temporal variability
in water temperature should be a priority for habitat
investigations.
Another habitat feature that had an important role
in each of our models was water currents. Although
adult fish are generally able to avoid strong currents,
or maintain their position against such currents, the
survival of eggs and the distribution and survival of
larvae and early juveniles is often strongly affected
by water currents (e.g., Houde 1969; Clady 1976).
Unfortunately, data directly measuring water current
velocity in the Great Lakes or their tributaries are
often lacking, and tend to be expensive to collect.
Moreover, water velocity and direction in the Great
308 Rev Fish Biol Fisheries (2009) 19:295–312
123
Lakes is very dynamic, often changing over the
course of hours to days, and over short spatial scales
(e.g., 1–2 m within a vertical profile). Circulation
models are a helpful tool to address this problem
(e.g., Beletsky et al. 2007; Zhao et al. 2009), but are a
major undertaking to construct, implement and
validate.
Model scale and validation
We have focused on process-driven, mechanistic
models, in contrast to aggregated, holistic models,
such as the morphoedaphic index (Fig. 1). We do not
mean to imply that the mechanistic modeling excludes
aggregated modeling—rather, the two approaches
should be able to inform one another. For example,
the process-driven models can help identify mecha-
nisms that give rise to the observed aggregate patterns.
Likewise, the observed aggregate relationships are
important validation data for the mechanistic models.
A strength of our modeling approach is that the model
explicitly makes predictions for many system attri-
butes (Fig. 1), ranging from process rates (e.g., birth
rate, death rate, individual growth rates) to detailed
descriptions of system characteristics (e.g., distribu-
tion of fish), to more aggregated descriptions of the
system (e.g., total biomass, abundance, population
size structure). Comparison of model predictions
across this range of scales with data helps to validate
the models and point to the specific areas where there
are problems. Further, such a modeling approach is
well suited to utilizing the results of field experiments
as a means of validating or calibrating specific parts of
the model. This is in contrast to regression-based
approaches where it is often difficult to pinpoint how
and why the regression may not fit. Despite the
conceptual appeal of performing model validations at
several levels of aggregation, trying to validate our
models has been a very challenging task because of
the limited data available for validation. Further, a key
use of these models is to predict the response of fish to
changes in habitat conditions; conditions that may be
outside the range of data used to construct the models.
As such, validating models for this purpose may be
virtually impossible. This does not imply that such
models are useless; rather, the use of these models (or
any other model used to make similar predictions)
should recognize the uncertainty associated with such
situations.
While constructing our models, we often made
trade-offs between basing the model on data versus
using assumptions. Because of the lack of published
studies, we were often ‘‘forced’’ into making assump-
tions to develop even the most basic model. A hard
question to answer is ‘‘when are there so many
assumptions, or when are the assumptions so strong,
that the model predictions are not informative?’’ An
associated question is how to make good assump-
tions. One approach for making assumptions that we
found particularly appealing was to use general
principles from ecology or other basic sciences as
the basis for making an assumption. For example,
over long time periods, fish evolution is a shaping
force. This can be a problem because fish can adapt
(thereby altering the parameters describing how their
vital rates vary with habitat conditions) to changing
habitat conditions. In the short term, however, we can
use the assumption that fish have adapted to present
habitat conditions, and tend to behave in an optimal
way (i.e., maximize fitness) to help constrain some of
our modeling problems. For example, we used the
idea that smallmouth bass spawning distribution will
follow the ideal free distribution (e.g., Fretwell and
Lucas 1970; Tyler and Hargrove 1997). We assume
that they are behaving to maximize fitness, and that
some principles of optimal habitat selection guide
fish distribution if they are able to actively choose
among potential habitats. Another example of using
basic ecology to constrain our models is the appli-
cation of life history theory (e.g., Jensen 1998)to
help ‘‘tie together’’ different life stages in our models
(Shuter et al. 1998). Constraints imposed by life
history consideration helped us to infer ages and sizes
at maturation of walleye (for example) under differ-
ent growth regimes imposed by different habitat
conditions. These theories help by creating con-
straints in the modeling problem, making it much
more feasible to find reasonable solutions.
Conclusion
In summary, we feel that the modeling approach we
describe holds promise for bringing forward a useful
marriage between the tradition of fish population
dynamics and habitat science. Many practical con-
cerns remain, however, not the least of which is the
lack of solid quantification of the response of
Rev Fish Biol Fisheries (2009) 19:295–312 309
123
population vital rates and fish behavior to habitat
conditions, and the scarcity of data on habitat
conditions at a broad temporal and spatial scale.
Another challenge not listed above is how to bring
this modeling approach into a true multi-species
application, where fish populations interact fully with
their prey, other populations of fish, and the habitat in
which they all reside.
Acknowledgments The authors thank the Great Lakes
Fishery Commission for providing principal support for this
project, and extend their thanks to the Michigan Department of
Natural Resources, the Department of Fisheries and Oceans,
and the Ontario Ministry of Natural Resources for
supplemental funding for this research. The authors also
thank the Great Lakes Fishery Commission’s Board of
Technical Experts for their constructive comments on this
project since its inception. This is contribution number 2009-05
of the Quantitative Fisheries Center at Michigan State
University.
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